Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus

I. Kovács, Kata Miháltz, Kinga Kránitz, Éva Juhász, A. Takács, Lóránt Dienes, Róbert Gergely, Zoltán Z. Nagy

Research output: Contribution to journalArticle

16 Citations (Scopus)


Purpose To describe the topographic and tomographic characteristics of normal fellow eyes of unilateral keratoconus cases and to evaluate the accuracy of machine learning classifiers in discriminating healthy corneas from the normal fellow corneas. Setting Department of Ophthalmology, Semmelweis University, Budapest, Hungary. Design Retrospective case-control study. Methods Patients with bilateral keratoconus (keratoconus group), clinically and according to the keratoconus indices of the Pentacam HR Scheimpflug camera; normal fellow eyes of patients with unilateral keratoconus (fellow-eye group); and eyes of refractive surgery candidates (control group) were compared. Tomographic data, topographic data, and keratoconus indices were measured in both eyes using the Scheimpflug camera. Receiver operating characteristic (ROC) analysis was used to assess the performance of automated classifiers trained on bilateral data as well as individual parameters to discriminate fellow eyes of patients with keratoconus from control eyes. Results Keratometry, elevation, and keratoconus indices values were significantly higher and pachymetry values were significantly lower in keratoconus eyes than in fellow eyes of unilateral keratoconus cases (P

Original languageEnglish
Pages (from-to)275-283
Number of pages9
JournalJournal of Cataract and Refractive Surgery
Issue number2
Publication statusPublished - Feb 1 2016


ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Surgery

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